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In the course of a scientific work, I will discuss the different types of reinforcement learning. However, I have difficulties to find these different types.

So, into which subcategories can reinforcement learning be divided? For example, the following subdivisions seem to be useful

  • Model-free and Model-based
  • Dynamic Programming, Monte Carlo and Temporal Difference

Any others?

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Your two suggestions are not mutually exclusive. If you go by this process, you'll have to do a "Cartesian product" of a bunch of different RL categorizations which would get out of hand. I recommend, if you can, to describe some sort of "RL taxonomy" instead. By this I mean describing different RL characterizations without assuming they're mutually exclusive.

To add to your list :

  • On-policy or off-policy
  • Value based or policy gradient
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    $\begingroup$ Maybe you could include a diagram like this one. It's your choice, but sometimes diagrams help. $\endgroup$ – nbro Jul 3 at 21:42
  • $\begingroup$ +1 for the diagram nbro posted, that's a great visualization for what I was trying to describe $\endgroup$ – harwiltz Jul 4 at 0:07

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